A quantitative blueness index for oligotrophic waters: Application to <scp>L</scp>ake <scp>T</scp>ahoe, <scp>C</scp>alifornia–<scp>N</scp>evada
Bibliographic record
Abstract
Abstract The perceived blue color of a lake often contributes to its aesthetic appeal, and changes in blueness can be indicative of major shifts in water quality. We developed a quantitative blue water index (Bw) for natural waters, and used it to evaluate spatial and seasonal variations in ultraoligotrophic Lake Tahoe, where clarity and blueness are of ecological and economic value and a focus for lake management strategies. Spectral reflectance was measured using a profiling hyperspectral radiometer, and the values were converted to the axis values of a color space: the International Commission on Illumination L*a*b*, where L* is the lightness of color, a* ranges from green to magenta, and b* ranges from blue to yellow. The blue water index Bw, defined as negative b*, was similarly high at two offshore monitoring sites in Lake Tahoe, but much lower in a semienclosed bay and a small adjacent lake. Seasonal variations of Bw were determined using a hyperspectral radiometer attached to a buoy in the middle of Lake Tahoe. The Bw values were highest in summer, and there was a strong inverse correlation between Bw and phytoplankton concentrations as measured by in vivo chlorophyll a fluorescence. However, there was no significant correlation between Bw and Secchi depth. Blueness and visual clarity are complementary measures of the perceived optical state of natural waters, and for many lakes may provide a powerful combination of indicators for conveying water quality to the public.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".